BoW2B - ECIR 2021 tutorial video pitch

Описание: Advances from the natural language processing community have recently sparked a renaissance in the task of adhoc search. Particularly, large contextualized language modeling techniques, such as BERT, have equipped ranking models with a far deeper understanding of language than the capabilities of previous bag-of-words (BoW) models. Applying these techniques to a new task is tricky, requiring knowledge of deep learning frameworks, and significant scripting and data munging. In this full-day tutorial, we build up from foundational retrieval principles to the latest neural ranking techniques. We provide background on classical (e.g., BoW), modern (e.g., Learning to Rank) and contemporary (e.g., BERT, doc2query) search ranking and re-ranking techniques. Going further, we detail and demonstrate how these can be easily experimentally applied to new search tasks in a new declarative style of conducting experiments exemplified by the PyTerrier and OpenNIR search toolkits. This tutorial is interactive in nature for participants; it is broken into sessions, each of which mixes explanatory presentation with hands-on activities using prepared Jupyter notebooks running on the Google Colab platform. At the end of the tutorial, participants will be comfortable accessing classical inverted index data structures, building declarative retrieval pipelines, and conducting experiments using state-of-the-art neural ranking models.